This disclosure relates generally to remote monitoring and diagnosing of industrial equipment and more particularly to optimizing storage and retrieval of monitoring data.
Generally, there are a lot of data generated from the remote monitoring and diagnosing of industrial equipment such as turbines, aircraft engines, locomotives, etc. Change detect monitoring data are one particular type of data that are of interest to industrial equipment monitoring specialists. Change detect monitoring are data that have typically passed through a compression algorithm such as a “swinging door compression”, the purpose of which is to reduce the amount of data stored in an archive by removing non-significant changes. The swinging door compression discards values that fall on a line connecting values that are recorded in the archive. When a new value is received by the system implementing the compression, the previous value is recorded only if any of the values since the last recorded value do not fall within the compression deviation area. The deviation area is a parallelogram extending between the last recorded value and the new value with a width equal to twice the compression deviation specification.
One particular area where change detect monitoring data are of interest is in the monitoring and diagnosing of a turbine. Typically, a multiple of sensors (e.g. 1000) collect data from the turbine using a commercially available change detect application. An on-site monitoring unit receives data from each sensor. The on-site monitoring unit then stores the data in a local archive. Generally, the on-site monitoring unit stores only data that represents a significant change from values of previous measurements in the local archive. Depending upon how a monitoring specialist has defined a significant change, it is possible that some sensors may have a measurement stored in the local archive every few seconds, while other sensors may have measurements stored at longer intervals such as every eight hours.
The local archive periodically transfers the data to a central site for subsequent monitoring and diagnosing by monitoring specialists and analytical tools. In order to adequately monitor the operation of the turbine, the monitoring specialists and analytical tools typically have to analyze a tremendous amount of data. Currently available database systems do not provide a storage and retrieval schema that can efficiently handle the large amount of data. Generally, these database systems use a simplistic storage schema such as tables to store the measurements collected from the sensors. Since there is a large amount of sensors providing data to the on-site monitor and local archive, there is a need for a large amount of storage. Providing enough storage to handle the increased demand can be quite expensive. With regard to the retrieval schema, the currently available database systems use indexes such as a B-tree index to provide the monitoring specialist and analytical tools with access to the stored data. Since there can be as many as 1000 sensors that provide data to the on-site monitor and local archive, there will be a need to perform a separate asynchronous query to access the data for each of the sensors since the two measurements just prior to and past the time period being examined must be retrieved to allow for correct interpolation. Processing 1000 separate and asynchronous queries is very central processing unit (CPU) intensive and storage intensive.
In order to overcome the above problems, there is a need for a database system that has an efficient storage schema that can minimize the size of data stored and provide indexes that enable access to the data in a timely fashion.